Affiliations: Institute of Manufacturing Engineering, Zhejiang
University, Hangzhou, Zhejiang, China
Note: [] Corresponding author: Dr. Cao Yanlong, Institute of
Manufacturing Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027,
China. Tel.: +86 571 87953198; E-mail: sdcaoyl@zju.edu.cn
Abstract: In order to reduce the false alarm rate and missed detection rate of
a Loose Parts Monitoring System (LPMS) for Nuclear Power Plants, a new hybrid
method combining Linear Predictive Coding (LPC) and Support Vector Machine
(SVM) together to discriminate the loose part signal is proposed. The alarm
process is divided into two stages. The first stage is to detect the weak burst
signal for reducing the missed detection rate. Signal is whitened to improve
the SNR, and then the weak burst signal can be detected by checking the
short-term Root Mean Square (RMS) of the whitened signal. The second stage is
to identify the detected burst signal for reducing the false alarm rate. Taking
the signal's LPC coefficients as its characteristics, SVM is then utilized to
determine whether the signal is generated by the impact of a loose part. The
experiment shows that whitening the signal in the first stage can detect a
loose part burst signal even at very low SNR and thusly can significantly
reduce the rate of missed detection. In the second alarm stage, the loose
parts' burst signal can be distinguished from pulse disturbance by using SVM.
Even when the SNR is −15 dB, the system can still achieve a 100%
recognition rate
Keywords: Alarm signal, Loose Parts Monitoring System, Support Vector Machine, Linear Predictive Coding
